BrandRamp.
06 — Product

Ship the AI product your roadmap keeps promising.

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You're here because

We'd bet one of these is yours.

Three moments we hear on almost every first call about ai applications. If one of them landed, that's usually where we'd start.

  • Your AI feature has been 'two weeks from shipping' for two quarters.

    Eng is heads-down on the previous quarter's commitment. The AI ticket keeps slipping. Marketing started the announcement three sprints ago. Investors asked again this week.

    01 · Roadmap · perpetually two weeks out

  • You shipped an AI feature and have no idea if it's getting worse.

    No evals. No regression checks. The model provider released a new version last week. Your support team is the canary in the coal mine — and the canary has been coughing.

    02 · AI in prod · zero observability

  • Investors keep asking about your AI roadmap.

    You keep keeping it directional. They keep asking. The board deck has 'AI-powered' on slide 6 and a screenshot of a feature you haven't shipped on slide 12.

    03 · Board narrative · thin

If any of that landed, we're cooking the same dish.

Mise en place

The ingredients laid out before we cook.

The stack we reach for on this dish. Not religion — we'll swap in what your kitchen already runs if it fits.

Next.js
Vercel AI SDK
Claude
Postgres
Stripe
Sanity CMS
  • Next.js
  • Vercel AI SDK
  • Claude
  • Postgres
  • Stripe
  • Sanity CMS
What's in the dish

From prototype to production. We design, build, and operate AI applications with the models, evals, and guardrails your team actually needs.

  • Vercel AI SDK + Claude architectures

  • RAG, evals, observability

  • Auth, billing, multi-tenant

  • Hand-off to your engineering team

This week, this dish

Two-week cycles. Real surfaces every Friday. You always know which stove we're on.

  1. Week 01

    Source

    Discovery + scope

    • Use-case clarity workshop with founders + engineering
    • Model + provider selection (Claude default; abstraction included)
    • Eval plan + safety guardrails

  2. Weeks 02–03

    Prep

    Architecture + design

    • Data model + RAG strategy
    • Auth, billing, multi-tenant if needed
    • Observability + cost monitoring wired before code

  3. Weeks 04–09

    Cook

    Build sprints, every two weeks

    • Working surface delivered each cycle, not slides
    • Regression evals on every release
    • Internal user testing each Friday

  4. Week 10

    Plate

    Launch readiness

    • Perf + accessibility + SOC2-friendly audit
    • Runbook + incident playbook handed off
    • Live observability dashboards transferred to your team

  5. Weeks 11+

    Serve

    Operate or hand off

    • Operating retainer (monitoring + evals + iteration), or
    • Clean hand-off with engineer-to-engineer transfer week
Investment

Application builds are scoped — pricing reflects the surface you ship. Below are starting investments; final quotes follow the discovery week.

  • Starter

    Eval suite for an existing AI feature

    $4,500

    / one-time

    Sleep better on something you already shipped. Regression evals + observability for a single AI feature.

    See on the menu
  • Most ordered

    Main

    The Signature Dish

    from $35,000

    / 8–14 week engagement

    Prototype-to-production AI build with the models, evals, and guardrails your team actually needs.

    See on the menu

  • Pair

    + The Studio Retainer

    from $18,000

    / monthly, post-build

    We operate what we built. Continuous evals, monitoring, and iteration without re-staffing.

Every engagement is quoted before it's confirmed. These are starting points, not contracts.

What you bring

The counter-prep. Light list — heavy when missing.

The smaller the prep list, the smoother the cook. Here's the minimum:

  • 01A real PRD or use-case doc — one paragraph beats a deck
  • 02API access to the systems the AI will touch (CRM, DB, etc.)
  • 03Brand voice / tone guidance, even informal
  • 04An engineering owner on your side for the hand-off week
  • 05Realistic eval expectations (we'll calibrate)

They shipped more in two weeks than our last vendor did in a quarter. The eval suite is the part we use every week now — that alone justified the bill.

Head of Engineering

Health Tech · Series B

The Recipe — universal

From a one-week audit to a full build, the cadence is the same.

  1. 01 · Source

    Discover

    Audit current state, identify the highest-leverage moves, set the scoreboard.

  2. 02 · Prep

    Architect

    Map the system: content model, data flows, integrations, agents, evals.

  3. 03 · Cook

    Build

    Ship in two-week increments. You see real surfaces, not slide decks.

  4. 04 · Plate

    Launch

    Quality bar: performance, accessibility, brand voice, schema, observability.

  5. 05 · Serve

    Scale

    Operate, measure, and compound — or hand off a system your team can run.

Stop us if you've thought any of this

We've heard it. Here's the real talk.

The three doubts that come up on every first call about ai applications. You're not the first to think them — and you're not wrong to.

  • You've thought

    “Our eng team can build this in-house.”

    Real answer

    They can. They're already late on the previous quarter's commitments. Most teams that try take 3-4 months and ship without evals. We ship in 6 weeks WITH evals — your engineers go back to the roadmap.

  • You've thought

    “We don't want vendor lock-in on AI.”

    Real answer

    Everything we build is yours. Clean TypeScript repo, runbooks, evals, monitoring, provider abstraction so you can swap models. You can take it the day we hand it off. No lock-in, by design.

  • You've thought

    “We need to figure out the right model first.”

    Real answer

    We do that in week 1 — with provider abstraction so you can swap later. Don't wait on the model decision to start the build. The build IS how you figure out the model.

Got a fourth? Bring it to the call.

Asked & plated

Most-asked, here.

Will you hand off the codebase?
+
Yes — everything we build is yours. Clean TypeScript repo, README, runbooks, evals, monitoring. We can also operate it for you on a retainer if that's the better path.
Which models and providers do you use?
+
We default to Anthropic Claude (Opus / Sonnet / Haiku) with provider abstraction so you can swap models. We use Vercel AI SDK, OpenRouter, or direct SDKs depending on the workload.
What about evals and observability?
+
Both are part of the build, not an afterthought. Every release includes regression evals and trace-level observability before it ships to production.
Pairs well with

The rest of the menu.

Yes Chef.

See the build play.

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